Abstract
In the multi-billion dollar formulated product industry, state of the art continues to rely heavily on experts during the “generate, make and test” steps of formulation design. We propose automation aids to each step with a knowledge graph of relevant information as the central artifact. The generate step usually focuses on coming up with new recipes for intended formulation. We propose to aid the experts who generally carry out this step manually by providing a recommendation system and a templating system on top of the knowledge graph. Using the former, the expert can create a recipe from scratch using historical formulations and related data. With the latter, the expert starts with a recipe template created by our system and substitutes the requisite constituents to form a recipe. In the current state of practice, the three steps mentioned above operate in a fragmented manner wherein observations from one step do not aid other steps in a streamlined manner. Instead of manually operated labs for the make and test steps, we assume automated or robotic labs and in-silico testing, respectively. Using two formulations, namely face cream and an exterior coating, we show how the knowledge graph may help integrate and streamline the communication between the generate, the make, and the test steps. Our initial exploration shows considerable promise.
Highlights
We encounter formulated products many times in our daily lives
Such a chart consists of various combinations of proportions of ingredients and conditions associated with actions; each combination corresponding to a unique formulation, which needs to be synthesized by the robotic equipment and tested subsequently
Historical formulations and related data spread over offline and online resources present an opportunity to aid the expert in generating new formulations
Summary
We encounter formulated products many times in our daily lives. Products like personal, home, industrial care, pharma and health care, coatings (paints) and surfaces (lubricants, adhesives), and confectionary foods and drinks are pervasive in their use. We show that if formulation making and formulation testing had automated or in-silico realizations, it is possible to use the knowledge graph as the connecting link between the three steps, reducing the siloed nature and benefiting from observations in each step. Using a knowledge graph as a central connecting artifact between aided formulation generation, automated formulation making, and in-silico testing, we aim to reduce the overreliance on experts and help integrate the largely siloed steps in formulation design. Both ways require the extraction of several different kinds of information and storage as a knowledge graph, as shown in Figure 1 and various analyses to be performed before proceeding with the generation of recommendations.
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